Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/80370
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Duan, Z | - |
dc.creator | Hou, S | - |
dc.creator | Poon, CS | - |
dc.creator | Xiao, J | - |
dc.creator | Liu, Y | - |
dc.date.accessioned | 2019-02-20T01:14:18Z | - |
dc.date.available | 2019-02-20T01:14:18Z | - |
dc.identifier.issn | 2076-3417 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/80370 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication: Duan, Z.; Hou, S.; Poon, C.-S.; Xiao, J.; Liu, Y. Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete. Appl. Sci. 2018, 8, 2171 is available at https://doi.org/10.3390/app8112171 | en_US |
dc.subject | Aggregate characteristic | en_US |
dc.subject | Artificial neural networks | en_US |
dc.subject | Input variable | en_US |
dc.subject | Recycled aggregate | en_US |
dc.subject | Recycled aggregate concrete | en_US |
dc.title | Using neural networks to determine the significance of aggregate characteristics affecting the mechanical properties of recycled aggregate concrete | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 8 | en_US |
dc.identifier.issue | 11 | en_US |
dc.identifier.doi | 10.3390/app8112171 | en_US |
dcterms.abstract | It has been proved that artificial neural networks (ANN) can be used to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates from different sources. This paper is a further study of the use of ANN to analyze the significance of each aggregate characteristic and determine the best combinations of factors that would affect the compressive strength and elastic modulus of RAC. The experiments were carried out with 46 mixes with several types of recycled aggregates. The experimental results were used to build ANN models for compressive strength and elastic modulus, respectively. Different combinations of factors were selected as input variables until the minimum error was reached. The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption and impurity content can reduce the prediction error of ANN to 5.43%. Moreover, for elastic modulus, water absorption and specific gravity are the most influential, and the network error with a combination of mixes, curing age, specific gravity and water absorption is only 3.89%. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied sciences, 2018, v. 8, no. 11, 2171 | - |
dcterms.isPartOf | Applied sciences | - |
dcterms.issued | 2018 | - |
dc.identifier.isi | WOS:000451302800161 | - |
dc.identifier.scopus | 2-s2.0-85056114132 | - |
dc.identifier.artn | 2171 | en_US |
dc.description.validate | 201902 bcma | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Duan_Using_neural_networks.pdf | 2.79 MB | Adobe PDF | View/Open |
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